The Time is Now for AI in Banking

Years ago, I had the good fortune to work for a bank with pristine credit quality. This squeaky-clean portfolio was fiercely protected by Ed, who was one of those classic, old-school credit guys. Ed had minimal formal credit training, and the bank didn’t rely on any sophisticated modeling or algorithms for monitoring risk. Instead, we relied on Ed’s gut instincts.

Ed had a way of sniffing out bad deals, and several of us looked forward to our weekly loan committee meeting, during which Ed would tear into the latest poor sap that dared to bring a wobbly deal for approval. After one of the more contentious meetings, I asked Ed how it was that he was able to quickly spot tiny flaws that our analysts had missed after hours of work, and why he was such a stickler about them. I’ll never forget his response.

“I learned this business in the ‘80s, and had to help clean up two banks before I was 40. The bad news was I had missed way too much time with my family. The good news was that I saw first-hand every conceivable way that a deal could bite you in the ass. And I’ve decided that I am NEVER doing another clean up.”

Ed couldn’t always put his finger on why a deal was bad, but he had learned to trust himself when something just felt “off.” We passed on a lot of deals based on those feelings, and our competitors gladly jumped on them. A whole lot of them ended up defaulting.

Obviously Ed wasn’t some kind of Nostradamus of banking. Instead, he was spotting patterns and correlations, even if he was doing it subconsciously. He knew he’d seen similar situations before, and that it had ended badly. Most banks used to be run this way. It was one of those approaches that worked well … until it didn’t.

When Ed’s Not Enough

Why? Because some banks didn’t have quite as good a version of Ed as we had. And some banks outgrew their Ed, and got big enough that they couldn’t give the personal smell test to every single deal. For much of the industry, we simply ran out of enough Eds who had cut their teeth in the bad times. A lot of banks were using an Ed who had never seen a true credit correction.

It turns out that humans are actually pretty bad at spotting and acting on patterns; the lizard brain leads us astray far more often than we realize. It was true even for us; Ed may have kept our portfolio safe, but if I’m honest, he did so at a huge opportunity cost. The growth we eked out was slow and painful, and being a stickler on quality meant we passed on a lot of profitable business.

This story probably rings true for a lot of bankers. The surprising thing isn’t that some banks still handle credit risk this way; the surprise is how many other kinds of decisions are handled the exact same way. Most banks have an Ed for credit, for pricing, for investments, for security, and for every other significant function they handle. And almost all of them are, when you get right down to it, flying by the seat of their pants.

Bankers have spent decades building ever more sophisticated tools for measuring, monitoring, and pricing risk, but eventually, in every meaningful transaction, a human makes the final decision. Like my old colleague, Ed, how many deals like this have they seen, and what was the outcome?

Other businesses look like this, too. A doctor diagnoses based on both the latest medical tests and their own judgement, which has been honed over hundreds or thousands of similar cases. A lawyer suggests legal strategy based on precedent and their own case history.

But in each of those examples, the human is limited by two things. First, how many experiences do they have that fit the exact same criteria? Usually it numbers in the dozens or low hundreds, and it’s not enough to be statistically significant. Second, are they pulling off the herculean task of avoiding all the cruel tricks our minds play on us? The lizard brain is a powerful foe to overcome.

AI’s Time Has Come

This shortcoming, in a nutshell, is why Artificial Intelligence (AI) and Machine Learning (ML) have become the latest craze in technology. We’ll call it AI for short (yes, we know that’s a misnomer, but we’ll save that for another post), and it is everywhere. Digital assistants like Siri, Cortana, and Alexa are popping up in new places every day, and they are actually learning as we interact with them. And it’s not just the bots; our photo software is learning to recognize family members, our calendars get automatically updated by things that land in our email, and heck, even our cars are learning how to drive.

AI has been possible, at least in some limited way, since the 1950s. But the proliferation of the cloud, and the ever-falling costs of both data storage and computing power that have come along for the ride, mean that now AI is a real thing that is commercially viable for all kinds of exciting applications. And that includes banking.

Banking & AI = Peanut Butter & Jelly

In fact, we think banking might just be the perfect use case for AI. All of those human decisions, influenced until now by gut feel and scattered data, can be augmented by machines like never before. In fact, AI can combine those disparate data sources and glean new insights that have been beyond the grasp of humans. These insights can then be presented to humans with real context, so that decisions are better, faster, and more informed.  It’s not about using machines to replace humans, but rather to free humans up to do what they do best.

The result will be banks that are more profitable, have less risk, and just as important, can provide customized service to their customers exactly how they need it and when they need it most. We believe AI will reshape every part of the banking business, and we are investing in our own business accordingly.

We feel strongly about this sea change, so you will start to see a lot more content from us around AI and the way that data can help you be better bankers. Because so much of this is new, though, it won’t just be about banking. We’ll also share some of the ideas we have been finding in other industries that we have found inspiring. Our goal is to learn everything we can about data insights and AI so our product and our clients can get better. We hope you’ll join us for the ride, and please let us know which things are most helpful.

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